Infrastructure-Based Smart Positioning System for Automated Shuttles Using 3D Object Detection

Automated vehicles need high positioning accuracy to execute driving maneuver effectively. This accuracy is crucial for the viability of dependent systems such as planning, decision-making, and perception. However, achieving precise localization typically necessitates expensive onboard sensors, that increase vehicle costs, complicate maintenance, and pose significant scalability challenges for large fleets of trucks or buses. To address these issues without compromising vehicle interoperability, this work proposes an infrastructure-based positioning system for critical areas. The system utilizes off- board sensors to collect data from a shuttle moving on a test track. The data collection is automated through a custom- designed labeling tool, eliminating the need for manual tagging. A deep learning model based on 3D object detection has been trained to localize the vehicle accurately during normal operation. Rigorous assessments have been conducted to evalu-ate localization performance, achieving an Average Trajectory Error of 0.17 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> for position, and 9.4 deg for rotation. To demonstrate real-world applicability, a complete architecture based on ROS2 was developed and tested with actual data, confirming its functionality in practical scenarios.